Why data quality trumps ML techniques in determining predictive model accuracy

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The AI market is booming, with the total global revenue for AI software, hardware, and services projected to soon reach $554 billion.

However, the majority of research and attention related to AI concentrates on complicated machine learning techniques and refining algorithm code. But data quality, not ML techniques, is often the deciding point between success and failure, with the data used to train algorithms much more impactful to predictive modeling accuracy than the technique used to build the model.

Read this e-book for a deep look into how data quality, breadth, and depth are crucial to building accurate predictive models, and why the budding data-centric AI approach is the new way forward.

Vendor:
Mobilewalla
Posted:
May 2, 2022
Published:
May 2, 2022
Format:
PDF
Type:
eBook
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